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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indian_names |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: my_awesome_wnut_model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: indian_names |
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type: indian_names |
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config: indian_names |
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split: train |
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args: indian_names |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9939821779886587 |
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- name: Recall |
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type: recall |
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value: 0.9958260869565217 |
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- name: F1 |
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type: f1 |
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value: 0.9949032781188464 |
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- name: Accuracy |
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type: accuracy |
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value: 0.999003984063745 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# my_awesome_wnut_model |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the indian_names dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0050 |
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- Precision: 0.9940 |
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- Recall: 0.9958 |
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- F1: 0.9949 |
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- Accuracy: 0.9990 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 66 | 0.0440 | 0.9579 | 0.9650 | 0.9614 | 0.9906 | |
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| No log | 2.0 | 132 | 0.0191 | 0.9870 | 0.9821 | 0.9845 | 0.9959 | |
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| No log | 3.0 | 198 | 0.0098 | 0.9919 | 0.9899 | 0.9909 | 0.9980 | |
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| No log | 4.0 | 264 | 0.0061 | 0.9927 | 0.9935 | 0.9931 | 0.9987 | |
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| No log | 5.0 | 330 | 0.0050 | 0.9940 | 0.9958 | 0.9949 | 0.9990 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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